{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b252cde9",
   "metadata": {},
   "source": [
    "# 第一回 Matplotlib初相识\n",
    "\n",
    "了解 Matplotlib 的容器、绘图方式。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfab79c5",
   "metadata": {},
   "source": [
    "## 一、认识 matplotlib\n",
    "<div align=\"left\">\n",
    "    <a href=\"https://matplotlib.org/stable/index.html\">\n",
    "    <img src=\"https://matplotlib.org/stable/_static/logo2_compressed.svg\" width=\"160\" height=\"80\">\n",
    "    </a>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c72ea7c",
   "metadata": {},
   "source": [
    "## 二、一个最简单的绘图例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "58903dad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T05:12:48.491773Z",
     "iopub.status.busy": "2021-11-16T05:12:48.490641Z",
     "iopub.status.idle": "2021-11-16T05:12:48.595095Z",
     "shell.execute_reply": "2021-11-16T05:12:48.594098Z",
     "shell.execute_reply.started": "2021-11-16T05:12:48.491725Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BWGf0bX9OlVNQwpubDzG2R2t6tw+wOo5HGd2jNVNGdOLDbUdZFqcDxlTNGGP4etcJxsz5npU7jvP4yM58/fiVdfavbHtuqtkWeFdEvKj4YfCJMeYrEXkeiDXGrATeAt4XkSQgE7jTaYkVAP/adIj8olJmjo20OopHmjUmkh1Hs/njZ7vp2a4Z3ds2szqSciPpuYU8+/keVu07Re+QAN57cCA92tXtf0Ni1QI7JibGxMbGWvLa7u5MfhHD/rmekd1aseDuaKvjeKyMvCKum7+Jxr5erHzsSpr5+VgdSbk4Ywyfxh7jb1/vo7i0nJljInnoyo4OHb0tInHGmJiqttN3qLqhNzYeorCkjBmjddXuTMH+DVlwdzSpWed48lMdMKYuLzWzgHvf2saTy3fRvW0zvp0+jMlXdbLsngpa7m4mPbeQ935K4eaoEDq3amp1HI83oGNznhrXjf/sPcmbm3TAmPq1snLD25sPM3bORnakZvP3m3vx0e8GERFs7d9Pe865Kxfy2oZkSsoM00d1sTpKvfHwsI7EHcnixf8coG9oIAM6Nrc6knIRiafyeHL5LrYfzebqrsH84ze9aRfYyOpYgK7c3crx7HN8sPUot8e0p0OLJlbHqTdEhH/e1ofQoEZM+yCe9LxCqyMpixWXljN/bSLXzd9MyumzzL2jH2/ff4XLFDtoubuVV9clATBtpK7a61ozPx8WTexPbmHFgLHSsnKrIymL7DqWzY0LNjN7dQLX9GrD6plXcXNUiMvNddJydxNHzxTwaWwqdw0IJcSFVgf1Sfe2zfj7zb3ZciiTV1YnWB1H1bHCkjJe+GY/Ny/8gayCYv712xhevSuKlk0bWh2tUnrO3U3MX5eIVwNh6tXOf2eburQJ/dsTdySTRRuSiQ4LYkyP1lZHUnVgy6EzPLV8FylnCrhrQChPXdudgEaufWmsrtzdQHJGPivij3HvoA60auZndZx67y839KRXSDNmfrKDo2d0wJgnyyss4Y+f7ebOxVsoN/DBwwN54ZY+Ll/soOXuFuatScTPx4tHRnSyOoqiYsDYonv600BEB4x5sHUHTjF2zkY+3HaUh6/syHczhjOks/vcDEfL3cUdPJnHl7uOc/+QcJc9t1cfhTZvzJw7+rL3eC5/XbnX6jjKgTLPFjPjo+08+E4s/n7eLJ8yhGev70EjXy+ro1WLnnN3cXNWJ9DU15tJwyOsjqIuMrJba6Ze3YmF65OJ7hDE7TGhVkdStWCM4ctdJ/jryr3kFZYwfVQXpl7dGV9v91wDa7m7sD1pOfxn70lmjO5CYGNfq+OoSswc05XtR7P50+d76NmuGT3b6YROd3Qyp2LQ15r9p+jbPoCXJgykWxv3Hhbnnj+S6onZqxMIaOTDg1d2tDqKugSvBsL8u6IIbOzDo0vjyTlXYnUkVQ3GGD7cdpQxs79nc1IGz17XnRWPDnX7Ygctd5cVfzSLdQfSmXxVhE4jdHEtmzZk4d3RpGWd4/ef7tQBY27iyJmz3P2vrTy9Yje9QgL4bsZwHh4WgVcD13ozUk1pubuo2asSaNHEl/sGh1sdRdkhJrw5T13bjVX7TrF44yGr46jLKCs3vLnpENfM3cietBxeuKU3H/xuoMeN9NBz7i5o66EzbE46zbPXdadJQz1E7uKhKzsSfzSLf353kH6hgQyM0HvEu5qDJysGfe1MzWZ091b8/ebetAnwzPeO6MrdxRhjeGVVAq38GzJxUAer46hqEBFeurUPHZo3ZtqH20nP1QFjrqK4tJy5axK4/tVNpGYWMP+uKP712xiPLXbQcnc5m5NOsy0lk2kjO+Pn417X1Srw9/PhtYnR5BWWME0HjLmEHanZ3PDqZuauSWR877asmXkVN/Zt53KDvhxNy92FnF+1hwQ24o4r9Jppd9WtTTNeuKU32w5n8vKqg1bHqbfOFZfxj6/3cctrP5BzroS37oth3p1RNG9SPy4r1hO6LmT9wXR2pGbz4i29aeitq3Z39puo9sSmZPHG94foHxbE2J5trI5Ur/yYfJqnlu/maGYB9wwM46lru+Ffz64603J3EedX7WHNG3Nr//ZWx1EO8OcberA7LYdZn+7kqzb+Hnc1hivKLSzhhW/28+G2VMJbNOajSYMYVE9/sa2nZVzEd3tPsvd4LjNGd8HHohvqKsdq6O3FwrujaSDCI0vidcCYk63Zd4oxs7/n459TmTw8gm+nD6+3xQ5a7i6hrNwwe3UCnYKbcFO/EKvjKAcKbd6YuXf0Y/+JXP70+R6r43ikM/lFPPbhdh5+L5agxr58PnUoT4/v7naDvhxNT8u4gK92HSfhVD4L7o7ymHfHqf9zdbdWPDayM6+uSyImPIg7rgizOpJHMMawcudx/rpyL/lFpcwcE8kjV3Vy20FfjlZluYtIKPAe0BowwGJjzLyLthkBfAEctn1qhTHmecdG9UylZeXMW5NItzb+jO/V1uo4yklmjI6sGDD2xV56tgugV4gOGKuN49nnePbzPaw7kE5UWCAv3dqHyNb+VsdyKfb8iCsFZhljegCDgKki0qOS7TYZY/rZPrTY7fTZ9jQOnT7LE2MiaaCrdo/l1UCYd2c/mjf21QFjtVBebliy5Qhj52zkp+Qz/Pn6Hix7ZIgWeyWqLHdjzAljTLztcR6wH9ATww5QXFrO/HWJ9A4JYKzei9PjtWjakIX3RHM8+xyzPtlJebkOGKuOw6fPcte/tvDs53voG1ox6OvBKzvqqcxLqNbJKREJB6KArZU8PVhEdorItyLS0wHZPN6ncamkZp5j5thIj3+3nKrQv0MQz4zvzpr9p3hDB4zZpbSsnDe+T2bc3I3sO5HLP2/tw5KHBhLWorHV0Vya3b9QFZGmwHJghjEm96Kn44EOxph8ERkPfA50qeR7TAImAYSF1e9fKhWWlLFgXRL9OwQxIjLY6jiqDj0wNJy4o1m8/N0B+oUGMrhT/b1cryr7T+Tyh+W72HUshzE9WvP3m3vRWm8Sbxe7Vu4i4kNFsS81xqy4+HljTK4xJt/2+BvAR0R+dSdZY8xiY0yMMSYmOLh+F9pH245yIqeQWWN01V7fnB8wFt6yCY/pgLFKFZWWMXvVQW54dTPHs8+x8O5oFt/bX4u9Gqosd6lonreA/caY2ZfYpo1tO0RkgO37nnFkUE9yrriMBeuTGRTR3K3upq4cp2lDb16f2J+zRaVM+2A7JTpg7BdxR7K4bv5m5q9L4sa+7Vj9xFVc16etLoKqyZ6V+1DgXmCkiOywfYwXkUdE5BHbNhOAPSKyE5gP3Gn0djSX9P6WFE7nFzFrbFeroygLRbb258Vbe7MtJZOXv9MBYwXFpTz35V4mvP4jBUWl/PuBK5h9Rz+C6smgL0er8py7MWYzcNkfmcaYBcACR4XyZPlFpSzakMzwyGCuCG9udRxlsZv6hRCbksXijYeIDgtiXK/6OWBsc+Jpnlqxi2NZ5/jt4A48Oa4bTfVGNbWi/+/VsXd+OExWQQmzxkRaHUW5iGev786utBx+/+lOurbxp2PL+jNgLOdcCf/4eh+fxB6jY8smfDJ5MAM66qLHEfR9unUo51wJizceYnT31vQNDbQ6jnIRFQPGovDyEqYsieNccf0YMPbd3pOMmf09y+PTmDKiE99OH6bF7kBa7nXorU2HyC2smIGh1IXaB1UMGDt4Ko9nP9+DJ//KKiOviKlL45n8fhwtmjbk80eH8odx3fTOYw6mp2XqSObZYt7+IYXrerelR7tmVsdRLmhE11Y8NrIL89cmEhMexF0DPOu9IMYYVsSn8fxX+zhXXMbvr+nKpOEROuLaSbTc68gbG5M5W1zKjNG/em+XUr+YPqoL249m8ZeVe+kd4jkDxtKyz/HMit18n5BB/w5BvHRrHzq3amp1LI+mPzLrQEZeEe/9eISb+rajiw44UpdRMWAsihZNfHlkSRw5Be49YKy83PDeTymMnf09P6dk8tcbevDp5MFa7HVAy70OLNqQTHFZOdNH67l2VbXmTXxZeE80p3ILmfnJDrcdMJackc8di3/iz1/sJbpDEN/NGM79Qzvq9NM6ouXuZCdyzrFk6xFujQ6pV5e4qdqJDgvij+O7s/ZAOou+T7Y6TrWUlpXz2oYkrp23iYMn83h5Qh/ee3AAoc110Fdd0nPuTrZwfRLGGB4bqefaVfXcNyScuKPZvLLqIFFhgQzp5PqjKvYez+EPy3exJy2Xa3u14bmbetLKX+fBWEFX7k6UmlnAxz+ncscVobpqUdUmIrx4S28igpvy+IfbOZnjugPGCkvKePm7A9y44AdO5hSx6J5oFk3sr8VuIS13J3p1XSIiwrSrddWuaqZJQ29enxhNQXEZ0z6Id8kBY7EpmYyfv4mF65P5TVQIa2YO59reestIq2m5O8nh02dZHp/GxIEdaBOgqxdVc51b+fPirX2IPZLFS98esDrOL84WlfLXlXu57Y2fKCop570HB/D/butLYGMd9OUK9Jy7k8xbk4CvVwOmjOhkdRTlAW7s2464lEze3HyY/h2CLF8Zb0zI4OkVuzmec477Bofz+2u60kQHfbkUPRpOkHgqjy92Hmfy8E4E+ze0Oo7yEH+8rgc7j+Xw+2W76NrGn4jgur9WPLugmL9/vZ9lcceICG7Cp5MHE6PTTV2SnpZxgrlrEmni683k4RFWR1EexNe7AQvvicbHS5iyJJ6C4tI6ff1vd59g9OyNfLY9jWlXd+abx4dpsbswLXcH23s8h693n+DBoeF6kwHlcCGBjZh3ZxQJ6Xk8+1ndDBhLzy3kkffjmLI0ntbNGrJy2lD+55quOujLxelpGQebszqRZn7ePDRMV+3KOYZHBjN9VBfmrkmkf3gQ9wzs4JTXMcawLO4Yf/tqH4Wl5fxhXDd+N6wj3jroyy1ouTvQjtRs1uw/xf+MjSSgkY/VcZQHe3xkF+KPZvPcyn30DgmgT3vH3h8gNbOAZz7bzabE01wRHsSLt/ahkwXn+FXN6Y9gB5q9OoHmTXy5f2hHq6MoD9eggTD3jn60bOrLlCXxZBcUO+T7lpcb3vnhMNfM3Uj8kSz+dlNPPp40WIvdDWm5O8jPKZlsTMjgkasi9N6Pqk40b+LLaxP7k55XyBMf137AWFJ6Hre98RN//XIfV4Q357snhnPv4HAd9OWmtNwd5JVVBwn2b8i9g8KtjqLqkX6hgfzp+h6sP5jBaxuSavQ9SsrKWbAukfHzNpOckc/s2/vyzgNX0D5IR2a4M11iOsCPSafZcqhiVnUjX72CQNWtewd1IO5IFrNXJxAVFsTQzvYPGNuTVnHd/P4TuVzXpy1/vaGnvjfDQ+jKvZaMMfy/VQdpG+DHnR52WzTlHkSEF27pTadqDBgrLCnjxW8PcNPCHzidX8Qb9/Zn4d3RWuweRMu9ljYkZBB/NJtpIzvrdb/KMo19vVk0sT+FJWVMrWLA2LbDmYyft4nXv09mQnR71jxxFdf0bFOHaVVdqLLcRSRURNaLyD4R2Ssi0yvZRkRkvogkicguEYl2TlzXYoxh9qoEQps34rb+oVbHUfVc51ZNeWlCH+KOZPHCN78eMJZfVMqfPt/D7W/8RHFZOUseGshLE/oQ0Fgv2/VE9pxzLwVmGWPiRcQfiBOR1caYfRdscy3QxfYxEFhk+1+PtmrfKXan5fDyhD74eus/gpT1ru/TjtiULN7+oWLA2HV9KgaMrT+Yzh9X7OZEbiEPDu3I/1wTSWNf/ZWbJ6vy6BpjTgAnbI/zRGQ/EAJcWO43Ae+ZivdCbxGRQBFpa/taj1RebpizOoGIlk34TVSI1XGU+sUz47uz61g2Ty7bSZuAhizdcpQV29Po0qopyx4ZQv8OQVZHVHWgWstNEQkHooCtFz0VAqRe8Odjts95rK93n+DAyTymj+6ib8dWLuX8gLGGPl7cuugnVu48zuMjO/PV41dqsdcjdv+7TESaAsuBGcaY3Jq8mIhMAiYBhIW575UlZeWGuWsSiGzdlBv6tLM6jlK/0jagEYvuiebfP6QwfXQXurdtZnUkVcfsKncR8aGi2JcaY1ZUskkacOFvFNvbPvdfjDGLgcUAMTExzh9n5yRf7EgjOeMsr0+M1nfvKZc1MKIFAyNaWB1DWcSeq2UEeAvYb4zBi1bqAAAK/UlEQVSZfYnNVgK/tV01MwjI8dTz7SVl5cxdk0jPds308jGllMuyZ+U+FLgX2C0iO2yfewYIAzDGvA58A4wHkoAC4AHHR3UNy+OOcTSzgLfvj6Hi555SSrkee66W2QxctsVsV8lMdVQoV1VUWsar65LoFxrI1V1bWR1HKaUuSS/zqIaPf04lLfscs8ZG6qpdKeXStNztVFhSxoJ1SQzo2JwrqzGYSSmlrKDlbqclW46QnlfErDG6aldKuT4tdzucLSpl0YZkhnVpqZeWKaXcgpa7Hd79KYUzZ4uZOSbS6ihKKWUXLfcq5BaW8Mb3hxjZrRVRYfrWbaWUe9Byr8Lbmw+Tc65EV+1KKbei5X4Z2QXFvLXpMON6tqFXSIDVcZRSym5a7pexeOMh8otLeUJX7UopN6Plfgmn84t458cUbujTjq5t/K2Oo5RS1aLlfgmvb0imsKSM6aO7WB1FKaWqTcu9EqdyC3l/yxFuiW5Pp+CmVsdRSqlq03KvxML1SZSVG6aP0lW7Uso9ablfJC37HB9tS+W2mFBCmze2Oo5SStWIlvtFFqxLBOCxkZ0tTqKUUjWn5X6BI2fO8knsMe4eGEa7wEZWx1FKqRrTcr/AvLWJ+HgJj47oZHUUpZSqFS13m6T0fD7fnsZvB4fTqpmf1XGUUqpWtNxt5q5JwM/Hi8nDI6yOopRStablDuw/kctXu07w4NCOtGja0Oo4SilVa1ruwJzVCfj7efO7YbpqV0p5hnpf7ruP5bBq3yl+NyyCgMY+VsdRSimHqPflPnv1QQIb+/DA0HCroyillMPU63KPO5LF+oMZTB7eCX8/XbUrpTxHleUuIm+LSLqI7LnE8yNEJEdEdtg+/uz4mM4xe/VBWjb15b4hHayOopRSDmXPyv0dYFwV22wyxvSzfTxf+1jO91PyGX5IOsOUEZ1p7OttdRyllHKoKsvdGLMRyKyDLHXGGMPs1Qdp08yPewaGWR1HKaUczlHn3AeLyE4R+VZEejroezrNpsTT/JySxdSRnfHz8bI6jlJKOZwjzkfEAx2MMfkiMh74HKh0ELqITAImAYSFWbNiNsbwyqqDhAQ24o6YUEsyKKWUs9V65W6MyTXG5NsefwP4iEjLS2y72BgTY4yJCQ4Oru1L18ja/ensPJbD9FFd8PWu1xcLKaU8WK3bTUTaiIjYHg+wfc8ztf2+zlBebnhldQLhLRpzS3SI1XGUUsppqjwtIyIfAiOAliJyDPgL4ANgjHkdmABMEZFS4BxwpzHGOC1xLfxn70n2n8hl7h398PbSVbtSynNVWe7GmLuqeH4BsMBhiZykrNwwZ3UCXVo15Ya+7ayOo5RSTlVvlq9f7jxOYno+M0ZH4tVArI6jlFJOVS/KvbSsnLlrEujethnX9mpjdRyllHK6elHuK+LTSDlTwMwxkTTQVbtSqh7w+HIvLi1n3tpE+rYPYHT3VlbHUUqpOuHx5f5JbCpp2eeYObYrtis2lVLK43l0uReWlLFgXRIxHYIY3qXS91UppZRH8uhy/2DrUU7mFjJLV+1KqXrGY8u9oLiU1zYkM6RTCwZ3amF1HKWUqlMeW+7v/XSE0/lFzBobaXUUpZSqcx5Z7nmFJbzxfTIjugbTv0Nzq+MopVSd88hy//cPKWQVlDBzjK7alVL1k8eVe05BCf/adIixPVrTp32g1XGUUsoSHlfub24+RF5hKU/oql0pVY95VLlnni3m7c2Hua5PW7q3bWZ1HKWUsoxHlfsb3ydzrqSMJ0ZXepc/pZSqNzym3NPzCnn3pxRu7hdC51b+VsdRSilLeUy5v7Y+mZIyw3RdtSullGeU+/Hsc3yw9Si39W9PhxZNrI6jlFKW84hyX7A+CYNh2sjOVkdRSimX4PblnppZwCc/p3LXgDDaBzW2Oo5SSrkEty/3eWsT8WogTL1aV+1KKXWeW5f7oYx8VsQf495BHWjdzM/qOEop5TLcutznrU3Ez8eLR0Z0sjqKUkq5FLct94Mn81i58zj3DQmnZdOGVsdRSimXUmW5i8jbIpIuInsu8byIyHwRSRKRXSIS7fiYvzZ3TQJNfb2ZPDyiLl5OKaXcij0r93eAcZd5/lqgi+1jErCo9rEub09aDt/uOcmDV3YksLGvs19OKaXcTpXlbozZCGReZpObgPdMhS1AoIi0dVTAysxZnUBAIx8eGtbRmS+jlFJuyxHn3EOA1Av+fMz2OafYfjSLtQfSmTQ8gmZ+Ps56GaWUcmt1+gtVEZkkIrEiEpuRkVGj72GA4ZHB3D8k3KHZlFLKkzii3NOA0Av+3N72uV8xxiw2xsQYY2KCg4Nr9GLRYUG89+AAmjT0rtHXK6VUfeCIcl8J/NZ21cwgIMcYc8IB31cppVQNVbn8FZEPgRFASxE5BvwF8AEwxrwOfAOMB5KAAuABZ4VVSillnyrL3RhzVxXPG2CqwxIppZSqNbd9h6pSSqlL03JXSikPpOWulFIeSMtdKaU8kJa7Ukp5IKm42MWCFxbJAI7U8MtbAqcdGMdKui+uyVP2xVP2A3RfzutgjKnyXaCWlXttiEisMSbG6hyOoPvimjxlXzxlP0D3pbr0tIxSSnkgLXellPJA7lrui60O4EC6L67JU/bFU/YDdF+qxS3PuSullLo8d125K6WUugyXLndXvTl3ddmxHyNEJEdEdtg+/lzXGe0lIqEisl5E9onIXhGZXsk2Ln9c7NwPtzguIuInIttEZKdtX56rZJuGIvKx7ZhsFZHwuk9aNTv35X4RybjguDxsRVZ7iIiXiGwXka8qec65x8QY47IfwHAgGthziefHA98CAgwCtlqduYb7MQL4yuqcdu5LWyDa9tgfSAB6uNtxsXM/3OK42P5/bmp77ANsBQZdtM2jwOu2x3cCH1uduxb7cj+wwOqsdu7PTOCDyv47cvYxcemVu3HBm3PXhB374TaMMSeMMfG2x3nAfn59z1yXPy527odbsP3/nG/7o4/t4+Jfpt0EvGt7vAwYJSJSRxHtZue+uAURaQ9cB7x5iU2cekxcutztUKc353aywbZ/in4rIj2tDmMP2z8jo6hYXV3IrY7LZfYD3OS42P75vwNIB1YbYy55TIwxpUAO0KJuU9rHjn0BuNV2ym+ZiIRW8rwrmAs8CZRf4nmnHhN3L3dPEU/FW4r7Aq8Cn1ucp0oi0hRYDswwxuRanaemqtgPtzkuxpgyY0w/Ku5hPEBEelmdqabs2JcvgXBjTB9gNf+3+nUZInI9kG6MibMqg7uXu90353Zlxpjc8/8UNcZ8A/iISEuLY12SiPhQUYhLjTErKtnELY5LVfvhbscFwBiTDawHxl301C/HRES8gQDgTN2mq55L7Ysx5owxpsj2xzeB/nWdzQ5DgRtFJAX4CBgpIksu2sapx8Tdy90jbs4tIm3On2sTkQFUHBeX/Itny/kWsN8YM/sSm7n8cbFnP9zluIhIsIgE2h43AsYABy7abCVwn+3xBGCdsf0mz5XYsy8X/f7mRip+X+JSjDFPG2PaG2PCqfhl6TpjzMSLNnPqManyHqpWEg+5Obcd+zEBmCIipcA54E5X/ItnMxS4F9htOy8K8AwQBm51XOzZD3c5Lm2Bd0XEi4ofQJ8YY74SkeeBWGPMSip+kL0vIklU/HL/TuviXpY9+/K4iNwIlFKxL/dblraa6vKY6DtUlVLKA7n7aRmllFKV0HJXSikPpOWulFIeSMtdKaU8kJa7Ukp5IC13pZTyQFruSinlgbTclVLKA/1/g9mCCeOhP9QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "'''Warning\n",
    "Since heavily importing into the global namespace may result in unexpected behavior,\n",
    "the use of pylab is strongly discouraged.\n",
    "Use matplotlib.pyplot instead.\n",
    "官方不建议使用pylab了。\n",
    "'''\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()  # Create a figure containing a single axes.\n",
    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Plot some data on the axes.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "698ec843",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T05:12:56.634936Z",
     "iopub.status.busy": "2021-11-16T05:12:56.634537Z",
     "iopub.status.idle": "2021-11-16T05:12:56.731517Z",
     "shell.execute_reply": "2021-11-16T05:12:56.730967Z",
     "shell.execute_reply.started": "2021-11-16T05:12:56.634905Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Matplotlib plot.\r\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29bec86a",
   "metadata": {},
   "source": [
    "## 三、Matplotlib 图像的组成部分\n",
    "\n",
    "一个完整的matplotlib图像通常会包括以下四个层级，这些层级也被称为容器（container）。\n",
    "\n",
    "- [Figure](https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure)：顶层级，容纳所有绘图元素。\n",
    "- [Axes](https://matplotlib.org/stable/api/axes_api.html#matplotlib.axes.Axes)：包含许多图形元素: [Axis](https://matplotlib.org/stable/api/axis_api.html#matplotlib.axis.Axis), [Tick](https://matplotlib.org/stable/api/axis_api.html#matplotlib.axis.Tick), [Line2D](https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text), [Text](https://matplotlib.org/stable/api/text_api.html#matplotlib.text.Text), [Polygon](https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Polygon.html#matplotlib.patches.Polygon) 和 坐标系 等。\n",
    "- [Axis](https://matplotlib.org/stable/api/axis_api.html#matplotlib.axis.Axis)：[Axes](https://matplotlib.org/stable/api/axes_api.html#matplotlib.axes.Axes) 的下属层级，用于处理所有和坐标轴、网格有关的元素。\n",
    "- [Tick](https://matplotlib.org/stable/api/axis_api.html#matplotlib.axis.Tick)：[Axes](https://matplotlib.org/stable/api/axes_api.html#matplotlib.axes.Axes) 的下属层级，用来处理所有和刻度有关的元素\n",
    "\n",
    "<div align=\"left\">\n",
    "    <img src=\"https://ai-studio-static-online.cdn.bcebos.com/c7263bb0352f416098405120e3cfbead9ece433db26b49668db641a31b91ada5\" width=\"550\" height=\"550\">\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f916ee2-6adc-4ac3-9efc-1b55ba908132",
   "metadata": {},
   "source": [
    "## 四、matplotlib有两种绘图接口\n",
    "\n",
    "1. 面向对象方式：首先显式地创建figure和axes这两个实例，然后通过调用这两个实例中的方法来绘图。\n",
    "2. matlab方法：通过pyplot自动创建figure和axes实例并绘图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fce36879",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T05:13:05.642240Z",
     "iopub.status.busy": "2021-11-16T05:13:05.641809Z",
     "iopub.status.idle": "2021-11-16T05:13:05.919459Z",
     "shell.execute_reply": "2021-11-16T05:13:05.918885Z",
     "shell.execute_reply.started": "2021-11-16T05:13:05.642208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JnG2t7Q8MAC40xgyvp93b1toBdR8v+qCugPLRqj18tOrUpg/G90viDxf3JKV1Sw9X5RvWWmYuzebzdbkn/bVBQYarh6SxeEcBWQcPe6E6EWmu3li8i935ZQQFGXoltXK6HK/x+s6Mtna8u7TuYWjdR2CPgTvg7WXZGAOXDkw+6a+NaRHKz87s7IWqfMMYw2e/HEXJKZ7hMGlQClHhIbRuYvOGIuKcD1bm8IeP1nPjGR3504TeTpfjVT7ZwtkYEwysANKBf1hrl9TTbJIxZjSwFfiNtTbbF7UFird+Poyi8lPfD6GyxsVna3LpnBjJwAA6Bx1qRxSCggwxLUJP6evbxURwwxkdPVuUiDRbi7bn87v31zKiczwPjOvpdDle55PFjNZal7V2AJACDDXG9DmuyadAR2ttP2AO8Gp9/RhjphpjlhtjlnvqYKVAYYw5rZW0BsOfP93Ax6sDa1vjw5U1nPnofD5fe/LTDkcrq6rhnWXZ7DhQeuLGIiIN2La/hFteX06H+Eiev35wwB70dDJ8+h1aawuB+cCFxz2fb639flecF4HBDXz9NGtthrU2IzEx0bvF+glrLROf/e7IWeanKiwkiM/vPJM/XtLLI3X5ytdbD5BzqJy4yNObNiivcnH/h+uYdQoLIkVEvvfYf7YSFhLEyzcOOeVRzkDj9akHY0wiUG2tLTTGtADOAx45rk17a+33bxknAJu8XVegWJtTxJqcIq4b3uG0+0qNC7zFjLPX5xEXGcaQjqc3XRIfFc7cu8bQMT7w/gxExH88dmV/9hwqD8ifp6fKFyMK7YH5xpi1wDJgjrX2M2PMX4wxE+ra3Fl36+Qa4E7gRh/UFRDmbtpHcJDhvF4/vmVxYz38xSYemb3ZI315W2WNi/mb93Nez7aEBJ/+X9VOCZFNarc0EfENl9vyj/nbKK2soWVYCF3bNq+j7H1x18NaYGA9zz901Of3A/d7u5ZANGfjPjI6tPbYTl85heUs3VnAved3P6mNi5ywaHs+JZU1XNDHMyGpxuXmDx+tp19KLD8dluaRPkWkabPW8udPN/Daol2ktG7BxAEnf+dZoGv6qzACWHZBGZvzSjw2mgBwTo82HCipDIjNl77cvJ8WocGc0SXBI/2FBAexbk8R76/M8Uh/ItL0vfjtTl5btIupozs3y5AACgp+be6mfQCc29NzQeGs7m0IMjCvrm9/Za1l3qb9jExPICI02GP9ntOjDat2H+LQ4SqP9SkiTdPna3P52xebuLhve+67sIfT5ThGQcGPzd9ygM6JkXT04JnmrSPDGNyhNXM37fdYn96Qub+UPYXlnN2jjUf7PatHG9wWvslsXrfXisjJqaxx8fCsTWR0aM1jV/b3+6lab1JQ8FPlVS4W78hnbDfP/qKE2l+WG3OL2V9c4fG+PWVeXZDxdFDolxJLXGQYX21RUBCRhoWHBDPj58N5YXKGR0c1A5GCgp9avCOfqho3Y7t7fr+IMd1q+/wm86DH+/aUMd0S+eMlvWgXE+HRfoODDGO6JfL11gO43NpJXESOta+4gn/M34bbbUmNa0nr09zDpSlQUPBTnRIiufOcrgzt5PnTyHq1b0VidDhfb/Xfd9W9klpx08hOXul7bPdECg5XsWGv/y/oFBHfKamo5saXl/Hc/G3sLihzuhy/oaDgpzomRHLXed28MuRljGF010S+zfTPd9Vb8kr4ast+ql1ur/Q/Kr32Lopv/XhERUR8q6rGzW1vrCRzXwnPXTfYo2vDAp2Cgh86UFLJV1v2U1Ht8to1Lh2YxE+Hpnn1GqfqrSW7uO2Nlbitd0JMfFQ4fZJb+fWIioj4jttt+d37a/lu20EevrzvkelZqeWT0yPl5MzdtI/7P1jH3LtGk97GOzuAndk1kTO7+uc/hvsu6skVg1MJD/HeAqKrhqRxsKTyxA1FpMnbnFfC52tzuef8bvwkI9XpcvyOgoIfunRAMmlxLemSGOXV61RUu8jcV0rflBivXudktQgL9npN13vg7AwRaRp6JbVi1q/PpLOmG+qlqQc/1CIsmJHpCV4/l+DJuZlc/s8FHK6s8ep1TsaXm/fx+H+2+GRKpKLaxc6Dh71+HRHxT5+vzeWjVXsA6JIYpbNgGqCg4Gcy95Xw2H+2sL/E+3scXDE4mX9dP5hQDxy45CkfrtrLjGXZhPvgjPfb3ljB1NeWe/06IuJ/Fm3P5zdvr+bNJbv8clG3P9HUg5+Zu2k/z3y5jetHeH9oPL1NtNfWQJwKt9uycNtBRndL9Emy//mZnamscWOt1TsJkWZk495ipr62nLT4lrwwOYPgZrzrYmMoKPiZhdsP0q1tFG2iPbvRUEM25xWzLOuQX8zZb9lXQv7hKkame+YQqBM5w0fXERH/kV1Qxg0vLyUqIoTXpgz12Mm8TZn/jDkLlTUulmUVeOy0xMaYu3EfD360nvxS5+8AWLCtdl+DM7rE++yaa7IL+c+GPJ9dT0ScNXfTPqpq3Lw2ZShJsS2cLicgKCj4kZW7CqmodvvsHTX89131oh35PrtmQxZtz6dTQqRP//H+65vt/OmTDVgv7dkgIv7lppGdmHPXaLq29Z9pV3+noOBHFm0/SJCBYZ09v21zQ/olxxAdHsKCbc4GhRqXmyU7C3w6mgAwoksCe4sq2JWv7VpFmqqqGje/nrmKtTmFAD6b2m0qFBQ8YPbs2XTv3p309HT+/ve/n3I/C7bn0y8lllYRoR6s7seFBAcxrHMcix0eUVi7p4jSyhqfTrsAjKwLJgu3Oz+iIiKe53Zb7n53DR+t3suWvBKnywlICgqnyeVycfvttzNr1iw2btzIjBkz2Lhx40n3U1pZw5rsQp+/o4bad9U7Dx5mb2G5z6/9vYV16xOG+3A0BWoP32rXKoKF23Xug0hTY63lz59u4NM1e7nvoh7adfEUKSicpqVLl5Kenk7nzp0JCwvj6quv5uOPPz7pfpZlFVDjtj5/Rw3/XTy4yMF31SHBQYztnkh8VLhPr2uM4Ywu8Szanq91CiJNzNPztvHqol38/MxO3DK6s9PlBCwFhdO0Z88eUlP/m1JTUlLYs2fPSfdTUlFDWlxLBndo7cnyGqV722jiIsMcHX6/dUwXXrlpqCPXHtElnvzDVWzZ58ywZGlljUKKiIe53JZ1ewq5fFAy91/UU3ulnAbto+Aj06ZNY9q0aQAcOPDDUwsn9E9iQv8kX5cFQFCQYUTneBZuP+jI5kOVNS7CgoMc+4c84vt1Ctvy6dGulc+v/8u3VlJe7WLm1BE+v7ZIU+R2W4KDDP+8bjBQ+zNOTp1GFE5TcnIy2dnZRx7n5OSQnJz8g3ZTp05l+fLlLF++nMRE/zu1cXiXeKpdbvIPV/n82s9+uY1Rj8ynqsbt82sDpLRuSYf4lo6NqIzvl8RlA3/4d0ZETt7XWw9w2T8XcqCkktDgIL/aoj5QaUThNA0ZMoTMzEx27txJcnIyM2fO5K233nK6rJN2VUYq1w1Lc+Rdfb+UWFxuS5gPzndoyBld4lmyo8CREZVJg1N8ej2RpmrFrgJufX0FnRIiHf150tR4PSgYYyKAb4Dwuuu9Z63943FtwoHXgMFAPnCVtTbL27V5QkhICM8++ywXXHABLpeLKVOm0Lt3b6fLOmlO/qM6r1dbzuvV1rHrA/zh4l60DAv2eUjYlFtMdEQIKa1b+vS6Ik3N5rxibnp5GW1bhfPqlKHEtPDdbeZNnS9GFCqBs621pcaYUOA7Y8wsa+3io9rcDByy1qYbY64GHgGu8kFtHjFu3DjGjRvndBmn7Z1l2by5ZBcf/mKkz+b09haW43JbUuOc/UUZGe7M4NpfPt3IobIqZv96tCPXF2kKdueXMXn6UlqGhfD6zcNIjPbt3VNNndffRtpapXUPQ+s+jl/iPRF4te7z94BzjJao+lxkeAhtW0VQUlnjs2u+vGAn5zz2NRXVLp9dsyFPz8vknnfX+Ox6FdUuVuw+5NMtu0WaopBgQ1pcS16/eajjbzqaIp+8jTLGBAMrgHTgH9baJcc1SQayAay1NcaYIiAeOHhcP1OBqQBpaWneLrvZubhfey7u196n11ywLZ9BHWKJCA326XXrU+O2VLt8d+z08qxDVNW4GZnu+022RJqC4opqIsNCSIptwbu3jtAtkF7ik4lpa63LWjsASAGGGmP6nGI/06y1GdbaDH+8c6CpKCqv9sl1Cg5XsSmvmJEObDJVn7vO68ZTVw/02Q+bBdsPEhJkGNpJQUHkZJVW1nD9i0v47XtrARQSvMinK9istYXAfODC417aA6QCGGNCgBhqFzWKjz0xZytnPDyPapf3b1Ws3bcBRnX1j6DwvfIq30yDLNx2kAGpsUQ5tD5CJFBVVLv42avLWL+3mIv6tHO6nCbP60HBGJNojImt+7wFcB6w+bhmnwA31H1+BfCl1VZ1jujZPprDVS5WZxd6/VoLth0kOiKEvskxXr9WY/1q5iqufmHxiRuepqLyatbtKTpyzLeINE61y80db61kyc4CHr+yP+c6fMdUc+CLEYX2wHxjzFpgGTDHWvuZMeYvxpgJdW2mA/HGmG3AXcB9PqhL6jG8czzGwHeZ3j8k6bttBxnROZ4QP9oQpUN8JOtyCikq8+70y6LtB3FbONPPRlNE/N0DH6xj7qb9/GViHyYO0EZlvuCLux7WWmsHWmv7WWv7WGv/Uvf8Q9baT+o+r7DW/sRam26tHWqt3eHtuqR+sS3D6Jccw3fbvBsUdueXkV1Q7nfTDqPSE3BbWLTDu9//N5kHiQoPYUBqrFevI9LU/CQjlYfG9+L64R2cLqXZ8J+3cuI3RndLZHV2oVcXNX6TWXvehb/dGjgwLZbIsGC+9fKIypId+QzvHK/tZUUawVrLil0FAAztFMeUUZ0crqh50U8p+YEzuybicluvHjv9XeZBkmNb0Dkh0mvXOBWhwUEM7xzPN5kHvHqi4yd3jOLPEwNvB08RJzwxZyuT/rmIhdu9PyUqP6SgID8wMK12Jf63mT885dJTHr+qPy/dOMQvb2ka0z2R7IJysvLLvHaNyPAQkmNbeK1/kabin19t5+kvt3FlRgojOutWYicoKMgPhAYHMaJLPF9v9d676pZhIXRvF+2Vvk/X2G5tAPh6y36v9P/3WZt5Y/Eur/Qt0pS8smAnj8zezIT+STx8eT+/fGPRHCgoSL3Gdk8k51A52w+UnrjxSZqxdDfPfpnp8X49JS2+JZ0SIvnGC+sUrLWszSkkc1+Jx/sWaUo25xXzp083cn6vtjx2ZX+CfXT+jPyQdnqRep3fqx1toiNIjvX8vukrdh1iz6Fy7ji7q8f79pQx3RKZuWw3FdUuj24vbYzhrZ8Px+3WNiEiP6ZHu1ZMu34wY7onatGvwxQUpF6J0eFeO/r5/37S3yc7P56Oq4emcmbXBI+/i3G5LcFBxmenc4oEmtnrc0mICiejYxzn99aui/5AMU0alHOojKfnZVJS4bnbJL9f8+Dv7xB6tGvFOT3berROay3jnvqWx/+zxWN9ijQlczfu4463VvHUvEyv3nUkJ8e/f1qLo/YWVvDE3K0e3c75F2+u5Hd1h7j4ux0HSnn+6+0e+4G1/UApW/aVkNgqwiP9iTQlX289wC/eXEnvpFb849pBWrjoRxQUpEGDO7Rm6QPncmZXz5zUebiyhnmb99MizPkjpRtjedYhHpm9mZ0HD3ukvzkba++iOLdnG4/0J9JULNx+kKmvLSe9TRSvTRlGq4hQp0uSo2iNgjQoOMiQGB3usf6+3nqAqho3FwbIaW8X92vPOT3bEB/lmT+DORvz6JscQ/sY7Z8gcrQPV+6hY3wkb/xsGDEtFRL8jUYU5EftKSxn8ktLWeCBsx9mr88jPjKMIR3jPFCZ90WGh3gsJOQVVbBydyHn66Q7kSO+n9Z7+PK+zJw6nLjIMIcrkvooKMiPio8MY0VWAZ+s3nta/VTWuPhy837O69U2oO6H3pJXwrUvLmbb/tPb9+CLdbkAjOvX3hNliQS8VbsPcdlzC9lfXEFIcBCtFRL8loKC/KiI0GDO792OWetzqaxxnXI/8zcfoLSyJmCmHb4X2zKUhdvz+Xxt3mn18/m6XHq2b0WXxCgPVSYSuNbmFDL5paUUHK7CpbsllCe6AAAgAElEQVQb/J6CgpzQhP5JFFfU8O3WU59++Hj1HhKiwhjlZ6dFnkjbVhEM6RjHx6v3nPLdD3sKy1mx6xDjNZogwvo9RVz34hJiWoQyY+pwrdkJAAoKckIj0xOIbRnKR6v3nNLXF1dUM2/zfsb3SyLEz/dPqM+kQcnsOHj4lG8T/XBlDgCX9EvyZFkiAWdzXjHXTV9CdEQoM34+XAejBYjA+6ktPhcWEsSE/kn8Z8M+Dh2uOumv/3xtLlU1bi4dmOyF6rzvor7tCQ8J4oOVpxaUrh6axjPXDCQt3vPbYYsEkoSocAakxjLj58NJjdO/h0ChoCCNcvWQNKpcbj5cdfK/LMf1ac+jV/Sjf0qMFyrzvlYRoZzfux0fr95DedXJr9NIiArnkv4aTZDmK+vgYapdbhKiwnnlpqEKzQFGQUEapVdSK/qlxDBz2e6TnquPaRnKlRmpAb3T2nXD0iiuqDnp6Zcn5mxl9vpcL1Ul4v825RZz2XML+MunG50uRU6RgoI02nXDOrB1XynfnsTxy89+mcnHp7i2wZ8M7RRHj3bRvLowq9FBqarGzaz1uazYdcjL1Yn4p025xVz74hLCQ4K5eVQnp8uRU6SgII02cWASf7usT6M3TKp2uZm9IY9Vuz13VoRTjDHcNLIjm/NKWLAtv1FfExYSxOxfjeau87p7uToR/7NxbzE/fWExYcFBzJw6nI4JkU6XJKdIWzhLo4WHBHPtsA6Nbh8aHMQnt4+issa/j5RurEsHJuO2MKRT6xO2PXS4iuBgQ6uI0IA520LEU6pdbm59YwURocHMnDqcDvEKCYFMIwpy0v69IY9bX1+B293wEHx2QRmHDlcRFGSazC/K8JBgrhmaRnhI8AmnHx799xbOe/xrKqpPfZMqkUAVGhzE09cMVEhoIrweFIwxqcaY+caYjcaYDcaYX9XTZqwxpsgYs7ru4yFv1+UJ7777Lr179yYoKIjly5c7XY7PlFTUsKewnIKy+m+VdLstd72zmknPL8T1I2EiUH299QAXP/0dReXV9b6+YlcBby/bzUV92hMR2jRCkkhjrMsp4pUFOwEYkBqrkNBE+GLqoQa421q70hgTDawwxsyx1h6/BPZba+14H9TjMX369OGDDz7glltucboUn7p8YDKXD0wmqIEzG57+MpNlWYd4dFK/gDrXobFatwwlPDSI4vJqYloce9JdUVk1d85YTVJsC+46v5tDFYr43ursQq6fXrvj4qTBKUTrqOgmw+sjCtbaXGvtyrrPS4BNQGDuvHOcnj170r1781uoFhRkCAoyFJVXc++7a47sWOhyW577ahtPzs1k0qAUfpKR4nCl3tEvJZYPbjuD1LiW5JdWUlZVA8DewnKumraI/SUVPHX1QFrpB6U0Eyt2HeL6F5fQumUYb98yQiGhifHpYkZjTEdgILCknpdHGGPWAHuBe6y1G3xYmpyCfcUVLNqRz/src+ibEkt+aSU5h8q5qE87Hr68b0Dvm3Ai339vf/p0I4u2H6RTQiSrswsJDQ7ipRuHMLjDiRc8ijQFS3cWcNPLS2nTKoK3fj5MZzc0QT4LCsaYKOB94NfW2uLjXl4JdLDWlhpjxgEfAV3r6WMqMBUgLS3NyxXXOvfcc8nL++HJgX/729+YOHFio/uZNm0a06ZNA+DAgQMeq89J3dpG8+kdo3hlYRYLth2kd1Ir7r2gOxP6JzXpkHC0G8/ogMvt5mBJFdcO68CUkZ2065w0KzsOlNI+tgVv/mwYbVtFOF2OeIE51RPxTuoixoQCnwH/ttY+3oj2WUCGtbbBnX0yMjKsvywgHDt2LP/3f/9HRkZGo9pnZGQ0q8WPcnr090X8UXFF9ZHptYpqlxbuBiBjzApr7Ql/cfnirgcDTAc2NRQSjDHt6tphjBlaV1fjdrURERGfmrtxH6P+/uWRXUcVEpo2X+yjMBK4Hjj7qNsfxxljbjXG3FrX5gpgfd0ahaeBq60vhjpO04cffkhKSgqLFi3i4osv5oILLnC6JBERr/piXS63vrGCTgmRdEnU7Y/NgU+mHrzBn6YeTlZCQgIdO3b8wfMHDhwgMTHR9wX5ieb8/f/Y956VlcXBg40/X0PEWz5atYe73lnNwLTWvHzTEN3ZE+AaO/WgLZwd0NAP/eY+F92cv//m/L1LYFiyI5/fvLOaYZ3imH7DECLD9eujudD/aREROaGMjnE8cFFPrh/RQWsSmhmd9SAiIg16dWEWewvLCQ4y/Hx0Z4WEZkhBwY9MnTrV6RIc1Zy//+b8vYt/stby6OzN/PGTDby+eJfT5YiDtJhRRESO4XZb/vzpBl5dtItrhqbxt0v7NHi2iwQuLWYUEZGTVuNy87v31/H+yhx+fmYnHhjXs9nstCr109SDn5g9ezbdu3cnPT2dv//9706X41NTpkyhTZs29OnTx+lSfC47O5uzzjqLXr160bt3b5566imnS5JmrrzaxabcYu46r5tCggCaevALLpeLbt26MWfOHFJSUhgyZAgzZsygV69eTpfmE9988w1RUVFMnjyZ9evXO12OT+Xm5pKbm8ugQYMoKSlh8ODBfPTRR83m/734j/IqF8bU7rKoLZmbB7/ZwllObOnSpaSnp9O5c2fCwsK4+uqr+fjjj50uy2dGjx5NXFyc02U4on379gwaNAiA6OhoevbsyZ49exyuSpqborJqrpu+hF/OWIW1ViFBjqGg4Af27NlDamrqkccpKSn6ZdEMZWVlsWrVKoYNG+Z0KdKM7C+p4Kppi1iXU8TlA5M11SA/oMWMIn6gtLSUSZMm8eSTT9KqVSuny5FmIrugjOunL2F/SSXTb8zgzK7Ncwt1+XEKCn4gOTmZ7OzsI49zcnJITk52sCLxperqaiZNmsS1117L5Zdf7nQ50kxYa7nl9RUcKqvmjZ8NY1Baa6dLEj+loOAHhgwZQmZmJjt37iQ5OZmZM2fy1ltvOV2W+IC1lptvvpmePXty1113OV2ONCPGGB6Z1I/QEEOPdhrFkoZpjYIfCAkJ4dlnn+WCCy6gZ8+eXHnllfTu3dvpsnzmmmuuYcSIEWzZsoWUlBSmT5/udEk+s2DBAl5//XW+/PJLBgwYwIABA/jiiy+cLkuasK+3HuDZLzMB6JsSo5AgJ6TbI0VEmolP1uzl7ndW07VNNB/84gzd3dDM6fZIERE54vVFWfxq5ioGprVm5i3DFRKk0bRGQUSkiXtqbiZPzN3Keb3a8sw1AxUS5KQoKIiINHFp8S24Zmgqf53Yh5BgDSTLyVFQEBFpgiqqXazNKWJopzguG5jCZQNTnC5JApSipYhIE1NUVs3105fUbqZUXOF0ORLgNKIgItKE7C0s54aXlpKVf5jHrxxAm1YRTpckAU5BQUSkidicV8yNLy3jcGUNr04ZyhldEpwuSZoABQURkSbiszW5WCzv3DqCnu21kZJ4hoKCiEiAK62sISo8hLvO68YNZ3QkMTrc6ZKkCfH6YkZjTKoxZr4xZqMxZoMx5lf1tDHGmKeNMduMMWuNMYO8XZeISKCz1vLCNzs457Gv2FtYTlCQUUgQj/PFiEINcLe1dqUxJhpYYYyZY63deFSbi4CudR/DgH/W/VdEROrhclv++tlGXlmYxbi+7YiLDHO6JGmiTjiiYIyZY4zpf6oXsNbmWmtX1n1eAmwCjj9DeSLwmq21GIg1xrQ/1WuKiDRlFdUufvHmCl5ZmMXNozrx7DWDtNuieE1jph5+BzxpjHn5dH95G2M6AgOBJce9lAxkH/U4hx+GCRERAZ6Yu5X/bNzHg+N78eD4XgQFGadLkibshFMPdaMBZxljJgGzjTEfAI9aa8tP5kLGmCjgfeDX1triUynWGDMVmAqQlpZ2Kl2IiAS8X57dleGd4zmrexunS5FmoFGLGY0xBthC7dqBXwKZxpjrG3sRY0wotSHhTWvtB/U02QOkHvU4pe65Y1hrp1lrM6y1GYmJiY29vIhIwFuWVcANLy2lrKr2DgeFBPGVxqxRWEDtL+0nqJ0OuBEYCww1xkxrxNcbYDqwyVr7eAPNPgEm1939MBwostbmNuo7EBFp4j5ds5drX1hCdkEZhWXVTpcjzUxj7nqYCmy01trjnv+lMWZTI75+JHA9sM4Ys7ruuQeANABr7fPAF8A4YBtQBtzUiH5FRJo0ay3PfbWd//33FoZ2jGPa5MHEttTdDeJbjVmjsOFHXr64EV//HfCjK23qQsjtJ+pLRKQ5eWJuJk/Py2TigCQevaIf4SG6s0F877T2UbDW7vBUISIicqxJg5KJCA3itjFdqJ3FFfE9HTMtIuJHduUf5pHZm7HW0iE+kl+MTVdIEEcpKIiI+IklO/KZ+I8FzFi6m5xDJ3UHuojXKCiIiPiBd5dnc930JcRFhvHRL0aSGtfS6ZJEAJ0eKSLiuGfmZfLYnK2MSk/gHz8dREzLUKdLEjlCQUFExGGDOrRm8ogOPDi+F6HBGugV/6KgICLigJxDZSzcns+VGamMTE9gZHqC0yWJ1EtBQUTEx1bsKuCW11dQ7bKc17MtrXVEtPgxjXGJiPjQeytyuGbaEqLCQ3j/tjMUEsTvaURBRMRHHp61iX99vYMRneP553WDtB2zBAQFBRERH2nfKoIbRnTgD1q0KAFEQUFExIu27S9lb2E5o7slcuPITk6XI3LSFGlFRLzky837uOwfC3jw4/VUu9xOlyNySjSiICLiYdZa/jF/G4/N2Uqv9q2YNjlDUw0SsBQUREQ8qNrl5s4Zq5i1Po9LByTx8OX9aBGm46ElcCkoiIh4UEiQoXVkGH+4uCc3j+qkkx8l4CkoiIh4wPwt+0mKaUH3dtH87dI+CgjSZGjSTETkNLjdlmfmZTLllWU8MWcrgEKCNCkaURAROUUlFdXc9c4a5mzcx8QBSfz98n5OlyTicQoKIiKnYG9hOddNX8Ku/DIeHN+LKSM7aiRBmiQFBRGRU5AQFU7XNlH87dK+jOgS73Q5Il6jNQoiIo1U7XLz1NxMDh2uIiwkiH9dn6GQIE2eRhRERBphf3EFd7y1iqVZBSRGh/PTYWlOlyTiEwoKIiInsGh7Pr+csYrDlTU8edUALh2Y7HRJIj7j9akHY8xLxpj9xpj1Dbw+1hhTZIxZXffxkLdrEhFprM/W7uXaFxfTqkUIH98xUiFBmh1fjCi8AjwLvPYjbb611o73QS0iIidlROd4rhvegd9e2IOocA3CSvPj9REFa+03QIG3ryMi4ilrsgv5zdurqXG5iY8K5y8T+ygkSLPlL3c9jDDGrDHGzDLG9Ha6GBFpnqy1vLxgJ1c8v5ClOwvILapwuiQRx/lDRF4JdLDWlhpjxgEfAV3ra2iMmQpMBUhL04pjEfGc4opqfvfeWmatz+Pcnm34v5/0J7ZlmNNliTjO8REFa22xtba07vMvgFBjTEIDbadZazOstRmJiYk+rVNEmrbb31zJfzbu44FxPXhhcoZCgkgdx0cUjDHtgH3WWmuMGUpteMl3uCwRaQastdS4LaHBQfzuwh5U1rgY3CHO6bJE/IrXg4IxZgYwFkgwxuQAfwRCAay1zwNXALcZY2qAcuBqa631dl0i0rwVllVxz7traRcTzv9c2pc+yTFOlyTil7weFKy115zg9WepvX1SRMQnlmcV8KuZq9lfUsH9F/XEWqsDnUQa4PjUg4iIr7jclufmb+OJuVtJad2S9249g/6psU6XJeLXFBREpNnYW1jOP7/eziX9k/ifS/sQHRHqdEkifk9BQUSavFW7DzEgNZbUuJbM/tVoUuNaaKpBpJEcvz1SRMRbyqtc/OGjdVz23EK+WJcHQFp8S4UEkZOgEQURaZI25RZz54xVZO4vZerozpzbq43TJYkEJAUFEWly3l62mwc/2kBMy1Bev3koZ3bVBm0ip0pBQUSanISocEZ3S+SRSX2Jjwp3uhyRgKagICJNwuz1ueQWVXDTyE6c07MtZ/doo7UIIh6gxYwiEtBKKqq559013PrGSj5dsxeXu3ZjV4UEEc/QiIKIBKwlO/K565015BaVc/tZXfj1ud0IDlJAEPEkBQURCUh5RRVcN30JSbEtePfWETrMScRLFBREJKDkFVXQLiaCdjERPH/dYIZ3jicyXD/KRLxFaxREJCDUuNw8My+TMx/9km8zDwBwTs+2CgkiXqZ/YSLi97btL+Xud1azJqeIS/on0SdJR0KL+IqCgoj4tdcXZfE/n2+iZVgwz/50IOP7JTldkkizoqAgIn7NGMOZXRP5f5f3oU10hNPliDQ7Cgoi4ldcbsvLC3YSFxnG5YNSuHZYGtcOS9O+CCIO0WJGEfEb2/aXcuW/FvE/n2/i28yDQO2IgkKCiHM0oiAijqtxuXnh2508MXcrLUKDefzK/lw2MNnpskQEBQUR8QNLswp4ZPZmLurTjj9P7K21CCJ+REFBRBxRUe1ixa5DjExP4IwuCXz4izMYmNba6bJE5DhaoyAiPrd0ZwHjnv6WG19eSm5ROYBCgoif0oiCiPhMcUU1j87ezBuLd5PSugXTbxhC+5gWTpclIj9CQUFEfKKi2sVFT35LblE5U0Z24u7zu2n7ZZEA4PV/pcaYl4DxwH5rbZ96XjfAU8A4oAy40Vq70tt1iYhvFJZVEdsyjIjQYG4d24V+yTH0T411uiwRaSRfrFF4BbjwR16/COha9zEV+KcPahIRL6txuXnpu52M/Pt/D3G6fngHhQSRAOP1EQVr7TfGmI4/0mQi8Jq11gKLjTGxxpj21tpcb9cmIt6xOruQ33+4jg17ixnTLZGO8ZFOlyQip8gfJgiTgeyjHufUPfeDoGCMmUrtqANpaWk+KU5ETs7//nszz321nTbR4Tx37SAu6tNOOyuKBDB/CAqNZq2dBkwDyMjIsA6XIyJ1rLVYC0FBhpTWLblhREfuPr8b0RGhTpcmIqfJH4LCHiD1qMcpdc+JSADYuq+EBz9az6UDk7lmaBrXDNVon0hT4g9B4RPgDmPMTGAYUKT1CSL+r7SyhqfnZfLSdzuJigghIlT7t4k0Rb64PXIGMBZIMMbkAH8EQgGstc8DX1B7a+Q2am+PvMnbNYnI6Zm3aR/3f7CO/SWVXJWRyu8u6kFcZJjTZYmIF/jirodrTvC6BW73dh0icvqstRhjCA4ytI+JYNrkDAbodkeRJs0fph5ExM8dOlzFY3O20LplGHef352x3dswumsiQUG6m0GkqVNQEJEG1bjcvLF4F0/MzaS0soYpIzseeU0hQaR5UFAQkXqtyS7k3vfWsHVfKSPT43lofG+6t4t2uiwR8TEFBRE5xvfrEFqEBVPtsvzr+sGc36utNk0SaaYUFEQEqD286al5mRw6XMWTVw+kW9to5t01RlMMIs2cgoJIM1dV4+b1xbt4el4mJRXVXD00DZfbEhxkFBJEREFBpDlbl1PEHTNWsiu/jDO7JvDAuJ70bN/K6bJExI8oKIg0Q+VVLlqEBZMUG0FiVDh/mdiHMd0SnS5LRPyQgoJIM7JtfymPzt7M/pJKPvzFGcRHhfPebWc4XZaI+DEFBZFmYF9xBU/OzeSd5dm0CA3m1jGdqXFbQoO1BkFEfpyCgkgTtyyrgOunL8Hltlw/vAO/PDud+Khwp8sSkQChoCDSBJVXucjKP0zP9q3omxzD1UPSmDKyE2nxLZ0uTUQCjIKCSBNSWeNixpLdPDt/O+EhQXx171giQoP504TeTpcmIgFKQUGkCah2uXl/RQ5Pz8tkb1EFwzrFce8F3QkNDnK6NBEJcAoKIk3Ad5kHue+DdfRPjeXRK/ozMj1eWy6LiEcoKIgEIJfb8tnavRSXV3P9iI6M7Z7Imz8bxhldFBBExLMUFEQCyPcB4Zkvt7FtfykZHVpz3fAOGGMYmZ7gdHki0gQpKIgEiIXbD/KHD9ez4+BhureN5rlrB3Fh73YaQRARr1JQEPFjVTVuyqpqiG0ZRnR4KOGhwfzz2kFc0LudDmwSEZ9QUBDxQxXVLmYu3c20b3ZwRnoC//eT/vRNieGLO0dpBEFEfEpBQcSPFFdU8+bi3Uz/bicHSyvJ6NCaS/onHXldIUFEfE1BQcSPPD03kxe/28mZXRO446yBDOsc73RJItLMKSiIOGjnwcO88O0OJvZPYljneH52ZmcmDkimb0qM06WJiADgk23bjDEXGmO2GGO2GWPuq+f1G40xB4wxq+s+fuaLukScsmLXIW55fTlnP/YV7y3PYXNeCQDtYiIUEkTEr3h9RMEYEwz8AzgPyAGWGWM+sdZuPK7p29baO7xdj4jTbntjBbPW5xHTIpTbx6Yz+YwOtImOcLosEZF6+WLqYSiwzVq7A8AYMxOYCBwfFESapNLKGj5evYerMlIJCQ5iVNcEhnaK48qMVCLDNfsnIv7NFz+lkoHsox7nAMPqaTfJGDMa2Ar8xlqbXU8bkYCxO7+M1xZl8faybEoqa0ht3ZLR3RK5dlgHp0sTEWk0f3k78ykww1pbaYy5BXgVOPv4RsaYqcBUgLS0NN9WKNJIRWXV3PPeGuZu2kewMVzUtz03j+rEgNRYp0sTETlpvggKe4DUox6n1D13hLU2/6iHLwKP1teRtXYaMA0gIyPDerZMkVNXWlnDlrwSBndoTXRECIcOV3H72HSuG96BdjFafyAigcsXQWEZ0NUY04nagHA18NOjGxhj2ltrc+seTgA2+aAukdO2bX8Jry/axfsr9xAcZFjywDlEhAbz7q0jtDmSiDQJXg8K1toaY8wdwL+BYOAla+0GY8xfgOXW2k+AO40xE4AaoAC40dt1iZyOlbsP8ciszSzZWUBYcBDj+7Xn+hEdCA+pveNYIUFEmgqfrFGw1n4BfHHccw8d9fn9wP2+qEXkVO3KP0xwkCGldUsA9haV89sLu3NlRioJUeEOVyci4h3+sphRxC9VVLv494Y8Zi7NZtGOfK4ZmsbDl/dlYGosX99zlk5wFJEmT0FBpAGPz9nKa4uyKCyrJqV1C+4+rxs/yahdl2uMQbMLItIcKCiI1Cksq2LOxn1cMTgFYwylFTWM7JLA1UNTGdklQaMHItIsKShIs1btcvP1lgO8vzKHeZv2U+Vy071dNP1SYnlwfE8tShSRZk9BQZqtTbnFXPviEgoOVxEfGca1w9OYNCiFPsm1hzIpJIiIKChIM5J18DAfr95LQnQY1w7rQOfESMZ2S2Rc3/aM6Z5IaLBPDlMVEQkoCgrSpOUWlfP52lw+XbOXNTlFGAM/GZzCtcM6EB4SzONXDXC6RBERv6agIE1OweEq4iLDAPjjxxv4z8Z99E2O4f6LejBhQBLtY1o4XKGISOBQUJAmYV9xBbPW5fLFujyW7yrg63vPIjWuJXef3537x/WkU0Kk0yWKiAQkBQUJaJvzinngg3Ws3F0IQPe20dx5TlciQoNrH7eLdrI8EZGAp6AgAcNay7o9RczduI8e7Vsxrm97EqPCqXFb7j6vGxf1bU96myinyxQRaVIUFMTvfbVlP3M27mPupn3sK64kyMCUkZ0Y17c98VHhfHLHKKdLFBFpshQUxO/kHCpj495izu/dDoBnvtzGptxixnRL5NyebTm7Rxta1y1WFBER71JQEMdVVLtYurOAr7ce4OutB9i2v5TQYMOqh84nKjyEJ68aQJtW4YSHBDtdqohIs6OgID7ndls25RXTIT6SqPAQXl6QxSOzNxMWEsSwTnFcPSSVsd3bEBlWGwxS41o6XLGISPOloCBeZ60lK7+MhdsPsmh7Pou255N/uIrnrxvEhX3ac0n/9vRoH83wTvG0CNOogYiIP1FQEI+z1rLj4GEAuiRGsfPgYc5+7GsA2rYKZ3S3REalJzCkYxwAKa1bktJaowYiIv5IQUE8Yl1OEcuyClix6xBLdhZwsLSSSYNSeOzK/nRKiOSRSX0Z0jGOTgmROmxJRCSAKCjISTtYWsnq3YUUlldzxeAUAH719ip2HDhMUkwEo9LjGdY5nhGd44HaUxivGpLmZMkiInKKFBSkUb5Yl8us9XmsyS5kd0EZAInR4UwalIwxhsevHECb6HCSYnWOgohIU6KgIEcUV1SzcW8x6/cUsXFvMZvySvjwF2cQERrM6uxCVmQV0C8llmuHpTEwrTV9k2OOTCMMSI11uHoREfEGBYVmqMblJiu/jK37ShjROZ7WkWG8tWQ3D3y47kibtq3C6Z0UQ1F5NRGhwfz2gu48MK6ng1WLiIgTFBSasIpqF9ZCi7Bgtu0v5al5mWTuK2HHgcNUudwAvDg5g3N7tWVgWiz3XtCd3kmt6J0UQ2J0+DF9hQQHOfEtiIiIwxQUApy1FmMMJRXVvLs8h6z8w2Tll5F18DDZh8r4n0v7cO2wDlhrWbX7EN3aRjOmeyLd2kTTvV30kUOUerZvRc/2rRz+bkRExN/4JCgYYy4EngKCgRettX8/7vVw4DVgMJAPXGWtzfJFbf7MWkuVy014SDAut2Xmst3sOVTO3sJycg6Vk32ojJ8MTuWeC7pjgb98tpHo8BA6JkTSLyWGywYm0z+ldu1A17bRfPe7s539hkREJOB4PSgYY4KBfwDnATnAMmPMJ9bajUc1uxk4ZK1NN8ZcDTwCXOXt2pxSVlVDfmkVBYerCA4y9EmOAeCx/2whK7+MAyUV7CuuJK+oggv7tOOJqwYQZODhLzZTUe2iXUwEybEtGN018cjXtooIZcUfziUuMkz7FIiIiMf4YkRhKLDNWrsDwBgzE5gIHB0UJgJ/qvv8PeBZY4yx1lof1HdEZY2LapfFWovbcuS/cXUnFe4rrqCovJrKajeVNS4qa9wEGcOILrX7Bcxal8uOg4cprayhpKKa0ooaEqPD+f3FvQC49sXFLMs6RFWN+8g1h3WK4+1bRgDw9dYDFJdXkxAVTq+kVpzdow2D0loDtXsRzL9nLHGRYQQH1R8E4qPC631eRETkVPkiKCQD2Uc9zgGGNdTGWltjjCkC4oGDPqjviA5cGCYAAAhhSURBVN9/uJ73VuQc81xMi1DW/PF8AP70yQZmrc875vXk2BYsuK92SH/Gsmy+2XqA0GBDdEQoUeEh9GgXfaTtmG61IwCxLcKIjwwjLjLsmH0HPrlj1I/Wd/wCQxEREW8LqMWMxpipwFSAtDTP7/Q3vl97urWNIqhu6N4YQ8ujDimaMqoT4/slER4SRHhoEOEhwce8/ty1gwgJMoSHBNU7/D91dBeP1ywiIuJNvggKe4DUox6n1D1XX5scY0wIEEPtosZjWGv/f3t3HyNXVcZx/PsLLTWlBFvX6gZqKUmD0qhp3RhFoosvoWLoasRkjZpiNBoEX6Ix8SUpSfEPE//QqDHYSBMh2IIVzGqK0NiqiU0rKyn0BYFlQaQxgXQVqGjJ4uMf96y5O527e2c7O/de9vdJJnvmnDOT8+xz7+6zd87ObAO2AQwMDHT9ZYnBi1cyePHKwvGpDzEqsmxJo+ouMzOzWfXin+PvA9ZKWiPpbGAYGGmZMwJsTu2rgb293p9gZmZmp5v3P4HTnoPrgXvI/j1ye0QclbQVGI2IEeBm4FZJY8AEWTFhZmZmFevJtfKI2A3sbunbkmv/B/hIL9ZiZmZm5fl9ec3MzKyQCwUzMzMr5ELBzMzMCrlQMDMzs0IuFMzMzKyQCwUzMzMr5ELBzMzMCrlQMDMzs0Jq6jslS3oG+GuXn7aPHn9iZQ84pmZwTM3gmJrBMZWzOiJePdukxhYK80HSaEQMVL2ObnJMzeCYmsExNYNj6i6/9GBmZmaFXCiYmZlZIRcK022regHzwDE1g2NqBsfUDI6pi7xHwczMzAr5ioKZmZkVWjCFgqSNkh6WNCbpa23Gl0i6PY0flHRhbuzrqf9hSVf0ct0zKRHTlyUdk/SgpN9KWp0be0nSoXQb6e3Ki5WI6RpJz+TW/unc2GZJj6bb5t6uvFiJmL6bi+cRSf/MjdUuT5K2S3pa0pGCcUn6for3QUkbcmN1zdFsMX0sxXJY0n5Jb86NPZH6D0ka7d2qZ1YipkFJz+aOry25sRmP2aqUiOmruXiOpPNnRRqra55WSdqXflYflfTFNnOqPaci4mV/A84CHgMuAs4GHgAuaZnzOeCm1B4Gbk/tS9L8JcCa9DxnNSSmy4GlqX3tVEzp/smqY5hjTNcAP2zz2BXAePq6PLWXNyGmlvmfB7bXPE/vBDYARwrGrwTuBgS8DThY5xyVjOnSqbUC75+KKd1/AuirOoY5xDQI/LpNf0fHbJ1iapl7FbC3AXnqBzak9rnAI21+7lV6Ti2UKwpvBcYiYjwiXgR2AkMtc4aAn6b2LuA9kpT6d0bEqYh4HBhLz1e1WWOKiH0R8UK6ewC4oMdr7FSZPBW5AtgTERMR8Q9gD7BxntbZiU5j+iiwoycrm6OI+AMwMcOUIeCWyBwAXimpn/rmaNaYImJ/WjM041wqk6ciZ3IezqsOY6r9uQQQEX+PiPtT+3ngIeD8lmmVnlMLpVA4H/hb7v5TnJ6I/8+JiEngWeBVJR9bhU7X9SmyinTKKySNSjog6YPzscA5KBvTh9Plt12SVnX42F4rva700tAaYG+uu455mk1RzHXNUadaz6UA7pX0Z0mfqWhNc/V2SQ9IulvSutTX+DxJWkr2C/MXue7a50nZS97rgYMtQ5WeU4u6/YRWP5I+DgwA78p1r46I45IuAvZKOhwRj1Wzwo78CtgREackfZbsKtC7K15TtwwDuyLipVxfU/P0siTpcrJC4bJc92UpRyuBPZL+kv7yrbv7yY6vk5KuBH4JrK14Td1yFfDHiMhffah1niQtIytsvhQRz1W9nryFckXhOLAqd/+C1Nd2jqRFwHnAiZKPrUKpdUl6L/BNYFNEnJrqj4jj6es48DuyKrZqs8YUESdycfwEeEvZx1akk3UN03KptKZ5mk1RzHXNUSmS3kR2zA1FxImp/lyOngbuoh4vTc4qIp6LiJOpvRtYLKmPhucpmelcql2eJC0mKxJui4g720yp9pyqagNHL29kV07GyS7rTm3OWdcy5zqmb2a8I7XXMX0z4zj12MxYJqb1ZJuS1rb0LweWpHYf8Cg12KxUMqb+XPtDwIHUXgE8nmJbntormhBTmvd6ss1Wqnue0noupHiT3AeYvvHqT3XOUcmYXke2P+nSlv5zgHNz7f3AxqpjKRnTa6eON7Jfmk+mnJU6ZusYUxo/j2wfwzlNyFP6nt8CfG+GOZWeUwvipYeImJR0PXAP2Y7e7RFxVNJWYDQiRoCbgVsljZEdZMPpsUcl3QEcAyaB62L6peFKlIzpO8Ay4OfZvkyejIhNwBuAH0v6L9lVpW9HxLFKAskpGdMXJG0iy8UE2X9BEBETkm4E7ktPtzWmX3asRMmYIDvedkY6+5Na5knSDrId832SngJuABYDRMRNwG6yXdpjwAvAJ9NYLXMEpWLaQrZn6UfpXJqM7AN6XgPclfoWAT+LiN/0PIA2SsR0NXCtpEng38BwOv7aHrMVhHCaEjFB9gfEvRHxr9xDa5sn4B3AJ4DDkg6lvm+QFae1OKf8zoxmZmZWaKHsUTAzM7M5cKFgZmZmhVwomJmZWSEXCmZmZlbIhYKZmZkVcqFgZmZmhVwomJmZWSEXCmbWVZL2SXpfan9L0g+qXpOZzd2CeGdGM+upG4Ct6cN31gObKl6PmZ0BvzOjmXWdpN+TvX34YEQ8X/V6zGzu/NKDmXWVpDcC/cCLLhLMms+Fgpl1jaR+4DZgCDgpaWPFSzKzM+RCwcy6QtJS4E7gKxHxEHAj2X4FM2sw71EwMzOzQr6iYGZmZoVcKJiZmVkhFwpmZmZWyIWCmZmZFXKhYGZmZoVcKJiZmVkhFwpmZmZWyIWCmZmZFfof9avgvtsgEWcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 466.019x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第1种方式\n",
    "import matplotlib as mpl\n",
    "\n",
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "w, h = mpl.figure.figaspect(0.618)\n",
    "fig = plt.figure(figsize=(w, h))\n",
    "\n",
    "ax1 = fig.add_axes([0., 0., 1.0, 1.0])\n",
    "ax1.plot(x, x**2, linestyle=\"--\")\n",
    "ax1.set_title(\"$y = x^2$\")\n",
    "ax1.set_xlabel(\"$x$\")\n",
    "ax1.set_ylabel(\"$y$\")\n",
    "\n",
    "ax2 = fig.add_axes([0.1, 0.6, 0.3, 0.3])\n",
    "ax2.set_title('$y = sin(x)$')\n",
    "ax2.plot(x, np.sin(2*np.pi*x), linestyle=\"-.\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4d91d50d-9a77-4e0d-94c7-9f2c60d9a33e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-16T05:13:09.492876Z",
     "iopub.status.busy": "2021-11-16T05:13:09.492340Z",
     "iopub.status.idle": "2021-11-16T05:13:09.681907Z",
     "shell.execute_reply": "2021-11-16T05:13:09.681206Z",
     "shell.execute_reply.started": "2021-11-16T05:13:09.492844Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 第2种方式\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0, 2, 100)\n",
    "\n",
    "plt.axes([0., 0., 1.0, 1.0])\n",
    "plt.plot(x, x**2, linestyle=\"--\")\n",
    "plt.title(\"$y = x^2$\")\n",
    "plt.xlabel(\"$x$\")\n",
    "plt.ylabel(\"$y$\")\n",
    "\n",
    "plt.axes([0.1, 0.6, 0.3, 0.3])\n",
    "plt.plot(x, np.sin(2*np.pi*x), linestyle=\"-.\")\n",
    "plt.title('$y = sin(x)$')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "511a1eea",
   "metadata": {},
   "source": [
    "## 五、总结\n",
    "\n",
    "了解到Matplotlib中的容器有：`Figure`、`Axes`、`Axis`、`Tick`。`Axes` 包含 `Axis`、`Tick`、`Line2D` 等图像元素。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe3a659c-e08b-4351-85f2-835ff18ec976",
   "metadata": {},
   "source": [
    "## 参考资料\n",
    "\n",
    "1. [第一回：Matplotlib初相识](https://datawhalechina.github.io/fantastic-matplotlib/%E7%AC%AC%E4%B8%80%E5%9B%9E%EF%BC%9AMatplotlib%E5%88%9D%E7%9B%B8%E8%AF%86/index.html)\n",
    "\n",
    "2. [Matplotlib API Overview](https://matplotlib.org/stable/api/index.html)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5bf2569bb356cce8976b63edee71ebcb84bd083587a0bf13e9d48d0116007ab7"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
